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A REVIEW ON SOFTWARE FAULT PREDICTION TECHNIQUE USING DIFFERENT DATASET

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File Size:
903.99 kB
Volume:
Volume 2, Issue 5 (May, 2016)
Publication No:
IJTC201605015
Author:
Heena Kapila, Daljit Kaur, Sachin Majithia
Downloads:
23 x

ABSTRACT
Software testing entails a number of processes that are focused on finding faults within a stipulated time. In this paper, different techniques have been discussed for finding the software faults prediction before the testing process. Numbers of researchers have been worked upon object oriented metrics and mostly concentrating on software fault prediction, very few has been published for bad smells. Bad code smells are used to identify complex classes in object-oriented software systems. Detection of bad code smell helps in refactoring. This review paper contributes to all code smell prediction techniques designed by researchers. The fault prediction model grants assistance during the software development.

Keywords:
Bayesian Inference, Bayesian Regularization, Levenberg-Marquardt, Public dataset, Fault Prediction, Software reliability, CK metrics

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